Navigating the airways of the lungs has always presented challenges to physicians attempting to diagnose and treat lesions transluminally. As such, numerous navigational aids and imaging tools have been developed and/or utilized to provide physicians a “map” of the lungs.
One such tool is a CT scanner. CT scanners use X-ray technology to take multiple scans or “slices” of the lungs. These scans each represent a cross-section of the lungs and can be viewed individually or assembled, via computer programs, to form a three-dimensional CT model. However, CT scans, like most images using X-ray technology, are somewhat cloudy and translucent in nature and difficult to view. As such, computer graphics techniques are employed to interpret the information provided by the CT model and “grow” a virtual model of the airways which mimics what might be seen by a bronchoscope navigating the airways. An example of this process is shown and described in U.S. patent application Ser. No. 11/939,537, entitled Adaptive Navigation Technique For Navigating A Catheter Through A Body Channel Or Cavity, the entirety of which is incorporated by reference herein.
This graphical technique is sometimes referred to as “region growing or 3D map generation,” and presents its own challenges. For example, region growing typically involves a processing of the CT data by analyzing each two-dimensional pixel, or, more pertinently, three-dimensional voxel, for brightness or “density” and assigning the voxel a value that indicates either tissue or air based on whether the density meets a certain threshold value. CT scans are grayscale images composed of a plurality of pixels (2D) or voxels (3D—if the scans have been assembled into a volume), each pixel or voxel varying in intensity from white (most intense) to black (least intense). Each intensity level between white and black appears as a shade of gray. By designating the various shades of gray from the CT scans either “tissue” or “air” the resulting image of the lungs becomes much more clear. However, if the voxels are designated incorrectly, the model of the lungs becomes inaccurate. Incorrect voxel designation results from setting the threshold level at an incorrect value, which is an inherent problem when attempting to assign discreet values (air or tissue) to voxels which are actually various shades of gray.
A presently-used technique for optimal threshold setting is beginning with a conservative threshold and performing a region-growing iteration. A conservative threshold is one that is not likely to result in leakage, which occurs when tissue is designated as air and creates a virtual image of the airways that looks like air (color) is spilling out of the airways. However, even with a conservative threshold, inaccuracies in the CT scans can result in “holes” after the segmentation process. These holes result in false branches.
Moreover, a conservative threshold results in airways that end prematurely. Therefore, after a conservative iteration is performed, resulting in a stunted branched structure, the threshold is incrementally increased and a second iteration is performed. These steps are repeated until leakage occurs. Thus, the maximum threshold that does not result in leakage is determined and used. This approach, however, naturally results in the least-dense portion of the CT image dictating the threshold level. There are other problems that arise from this method as well.
For example, during a full inhalation, the airways stretch and thin in order to accommodate the additional air volume. The thinning of the airways results in reduced tissue imaging density, and leakage thus arises even at lower threshold values.
Another example is that each time the threshold is increased, the algorithm runs from the initial seed point. Hence, if the threshold is increased ten times before leakage arises, each of the voxels analyzed in the initial iteration, is analyzed nine more times. This algorithm is thus inherently taxing on processing resources.
As such, a need is identified for a region-growing algorithm that is able to identify localized weaknesses in image data and “repair” them such that more distal branches of a bronchial tree can be segmented and “grown”.